My Databricks MCP server to interact with Databricks through LLM models
Overview
What is MCP Databricks?
MCP Databricks is a powerful integration that connects AI assistants to Databricks workspaces using the Model Context Protocol (MCP). It allows for efficient management of Databricks environments through AI-driven commands.
How to use MCP Databricks?
To use MCP Databricks, clone the repository from GitHub, configure your environment variables with your Databricks credentials, and choose your installation method (Docker or local installation). Start the server and connect it to your preferred MCP client.
Key features of MCP Databricks?
- Manage compute resources and clusters with precision.
- Execute SQL queries and analyze results.
- Organize and manipulate workspace objects.
- Comprehensive toolkit for library and command execution management.
Use cases of MCP Databricks?
- Automating cluster management tasks in Databricks.
- Executing SQL commands for data analysis.
- Managing libraries and workspace objects efficiently.
FAQ from MCP Databricks?
- What are the prerequisites for using MCP Databricks?
You need Python 3.11 or higher, a Databricks workspace, and a Databricks Personal Access Token (PAT).
- Can I use MCP Databricks without Docker?
Yes, you can install it locally using the provided instructions.
- What is the Model Context Protocol (MCP)?
MCP is a protocol that facilitates communication between AI assistants and Databricks workspaces.